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Tiago de Freitas Pereira authoredTiago de Freitas Pereira authored
siamese_image.py 9.26 KiB
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
import tensorflow as tf
from functools import partial
from . import append_image_augmentation, siamease_pairs_generator, from_filename_to_tensor
def shuffle_data_and_labels_image_augmentation(filenames, labels, data_shape, data_type,
batch_size, epochs=None, buffer_size=10**3,
gray_scale=False,
output_shape=None,
random_flip=False,
random_brightness=False,
random_contrast=False,
random_saturation=False,
per_image_normalization=True,
extension=None):
"""
Dump random batches for siamese networks from a list of image paths and labels:
The list of files and labels should be in the same order e.g.
filenames = ['class_1_img1', 'class_1_img2', 'class_2_img1']
labels = [0, 0, 1]
The batches returned with tf.Session.run() with be in the following format:
**data** a dictionary containing the keys ['left', 'right'], each one representing
one element of the pair and **labels** which is [0, 1] where 0 is the genuine pair
and 1 is the impostor pair.
**Parameters**
filenames:
List containing the path of the images
labels:
List containing the labels (needs to be in EXACT same order as filenames)
data_shape:
Samples shape saved in the tf-record
data_type:
tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
batch_size:
Size of the batch
epochs:
Number of epochs to be batched
buffer_size:
Size of the shuffle bucket
gray_scale:
Convert to gray scale?
output_shape:
If set, will randomly crop the image given the output shape
random_flip:
Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
random_brightness:
Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
random_contrast:
Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
random_saturation:
Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
per_image_normalization:
Linearly scales image to have zero mean and unit norm.
extension:
If None, will load files using `tf.image.decode..` if set to `hdf5`, will load with `bob.io.base.load`
"""
dataset = create_dataset_from_path_augmentation(filenames, labels, data_shape,
data_type,
gray_scale=gray_scale,
output_shape=output_shape,
random_flip=random_flip,
random_brightness=random_brightness,
random_contrast=random_contrast,
random_saturation=random_saturation,
per_image_normalization=per_image_normalization,
extension=extension)
dataset = dataset.shuffle(buffer_size).batch(batch_size).repeat(epochs)
data, labels = dataset.make_one_shot_iterator().get_next()
return data, labels
def create_dataset_from_path_augmentation(filenames, labels,
data_shape, data_type,
gray_scale=False,
output_shape=None,
random_flip=False,
random_brightness=False,
random_contrast=False,
random_saturation=False,
per_image_normalization=True,
extension=None):
"""
Create dataset from a list of tf-record files
**Parameters**
filenames:
List containing the path of the images
labels:
List containing the labels (needs to be in EXACT same order as filenames)
data_shape:
Samples shape saved in the tf-record
data_type:
tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
batch_size:
Size of the batch
epochs:
Number of epochs to be batched
buffer_size:
Size of the shuffle bucket
gray_scale:
Convert to gray scale?
output_shape:
If set, will randomly crop the image given the output shape
random_flip:
Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
random_brightness:
Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
random_contrast:
Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
random_saturation:
Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
per_image_normalization:
Linearly scales image to have zero mean and unit norm.
extension:
If None, will load files using `tf.image.decode..` if set to `hdf5`, will load with `bob.io.base.load`
"""
parser = partial(image_augmentation_parser,
data_shape=data_shape,
data_type=data_type,
gray_scale=gray_scale,
output_shape=output_shape,
random_flip=random_flip,
random_brightness=random_brightness,
random_contrast=random_contrast,
random_saturation=random_saturation,
per_image_normalization=per_image_normalization,
extension=extension)
left_data, right_data, siamese_labels = siamease_pairs_generator(filenames, labels)
dataset = tf.contrib.data.Dataset.from_tensor_slices((left_data, right_data, siamese_labels))
dataset = dataset.map(parser)
return dataset
def image_augmentation_parser(filename_left, filename_right, label, data_shape, data_type,
gray_scale=False,
output_shape=None,
random_flip=False,
random_brightness=False,
random_contrast=False,
random_saturation=False,
per_image_normalization=True,
extension=None):
"""
Parses a single tf.Example into image and label tensors.
"""
# Convert the image data from string back to the numbers
image_left = from_filename_to_tensor(filename_left, extension=extension)
image_right = from_filename_to_tensor(filename_right, extension=extension)
# Reshape image data into the original shape
image_left = tf.reshape(image_left, data_shape)
image_right = tf.reshape(image_right, data_shape)
#Applying image augmentation
image_left = append_image_augmentation(image_left, gray_scale=gray_scale,
output_shape=output_shape,
random_flip=random_flip,
random_brightness=random_brightness,
random_contrast=random_contrast,
random_saturation=random_saturation,
per_image_normalization=per_image_normalization)
image_right = append_image_augmentation(image_right, gray_scale=gray_scale,
output_shape=output_shape,
random_flip=random_flip,
random_brightness=random_brightness,
random_contrast=random_contrast,
random_saturation=random_saturation,
per_image_normalization=per_image_normalization)
image = dict()
image['left'] = image_left
image['right'] = image_right
label = tf.cast(label, tf.int64)
return image, label